Bearing combined failure diagnosis method based on improved impact dictionary matching pursuit

Abstract

The invention discloses a bearing combined failure diagnosis method based on improved impact dictionary matching pursuit. According to the method, a bearing vibration signal is iterated and decomposed successively to be a linear combination based on an i-item atom of an improved impact model dictionary; an improved model is established according to the type of an analyzed bearing, and can very accurately indicate out an impact signal of a fault bearing in the running process; a position parameter u in the improved model is used as a preferentially changing parameter according to the cycle characteristic of a bearing failure signal, an atom base with relatively low redundancy is established by using a method of gradually changing the parameters, and the search speed of an optimal atom is greatly improved; a analyzed signal is cut in the signal decomposing process, the optimal atom is searched from atoms in the atom base, and a corresponding impact signal component can be obtained by impact component reconfiguration; and then the failure characteristic frequency of a bearing is obtained by time-frequency transform, and thus the failure diagnosis of the bearing is achieved.

Description

technical field [0001] The invention relates to a bearing compound fault diagnosis method, in particular to a bearing compound fault diagnosis method based on an improved impact dictionary matching and tracking algorithm. Background technique [0002] Bearing is an important component of rotating machinery, and it is of great significance to detect its running state and fault diagnosis. The fault vibration signal is a typical non-stationary signal. Compared with the stationary signal, its distribution parameters or distribution rules change with time. In engineering practice, it is often encountered with non-stationary signals. Therefore, the research on such signals is very important for engineering. Application is extremely important. [0003] There are many types of bearing faults, which can be divided into rolling element damage, outer ring damage, inner ring damage, and composite fault forms of several damages, such as outer ring and rolling element damage, inner ring ...

AI Technical Summary

Problems solved by technology

[0006] However, for bearing vibration signals with complex faults, there are many background noise components, a large amount of data, and complex fault characteristic signals.

The matching pursuit algorithm for bearing fault diagnosis basically uses a single dictionary model that cannot establish a one-to-one correspondence with the parameters and operating status of the analyzed bearing, and there is still room for further improvement in the analysis effect

In terms of atomic selection, the redundancy of the dictionary has not been fundamentally reduced, and the analysis speed is slow

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